جستجوی استاد راهنمای دانشگاهی

• Production Engineering
• Tribology
• Materials Science
• +۵ مورد دیگر

Vladimir Kočović is an Associate Professor of Production Engineering at the Faculty of Engineering, University of Kragujevac. His research focuses on advanced manufacturing processes, tribology, materials science, and optimization of machining techniques. He holds a position in the Department of Production Engineering and has contributed to numerous interdisciplinary studies. His work spans experimental and computational methods, including tool wear analysis, material testing, and automation in manufacturing. He has developed specialized devices for measuring kinetic friction coefficients and optimizing cutting processes. Notable projects include studies on micro-cutting processes, 3D model reconstruction, and the application of AI in defect classification. Despite his prolific research output, no scientific awards are explicitly mentioned. He collaborates on projects involving tribological investigations, reverse engineering, and additive manufacturing. His academic activities include teaching and advising on mechanical engineering and industrial processes, though student names are not listed in available texts.

Arso Vukićevićمشاهده پروفایل

استادیار

• Industrial Engineering
• Artificial Intelligence
• Manufacturing Safety
• +۴ مورد دیگر

Arso Vukićević is an Assistant Professor in the Department of Production Engineering at the Faculty of Engineering, University of Kragujevac. His research focuses on integrating artificial intelligence, deep learning, and robotics into industrial safety, manufacturing processes, and ergonomic systems. Key areas include workplace safety monitoring, waste management automation, and medical device design optimization. He has contributed to over 30 peer-reviewed publications since 2011, emphasizing computational methods in biomechanics, predictive analytics for fracture risk, and Industry 4.0 technologies. His work spans applications like AI-driven personal protective equipment compliance systems, fatigue analysis of coronary stents, and modular lab setups for neuroergonomic studies. Vukićević’s research often bridges engineering and healthcare, such as developing software for coronary artery reconstruction and exploring AI’s role in public building energy management. Notable projects include frameworks for human-robot collaboration in manufacturing, smart warehouse management using machine vision, and evaluating exoskeletons for manual labor safety. His studies frequently employ deep learning, finite element analysis, and IoT sensors to address real-world industrial challenges.

Nenad Filipovićمشاهده پروفایل

استاد

• Applied Mechanics
• Applied Informatics and Computer Engineering
• Materials Science
• +۳ مورد دیگر

Nenad Filipović is a Professor at the Faculty of Engineering, University of Kragujevac, Serbia. He holds a position in the Department of Applied Mechanics and Automatic Control. His research focuses on applied mechanics, applied informatics, and interdisciplinary materials science, with particular emphasis on metal complexes, nanotechnology, and biomedical applications. His work spans structural chemistry, nanomaterial synthesis, and their applications in cancer therapy, antimicrobial agents, and energy storage systems. He has contributed to over 50 peer-reviewed articles since 2020, with notable studies on selenium nanoparticles, hydrogen storage materials, and computational methods in engineering education. Education: Formal academic qualifications not explicitly listed in text. Research Interests: Professor Filipović’s work integrates materials design with engineering applications. He explores the synthesis and characterization of novel inorganic complexes (e.g., Zn, Cd, Cu complexes) and their biological activities. His research also addresses environmental challenges through nanotechnology, including antimicrobial nanoparticles and microplastic toxicity studies. Recent projects involve computational modeling for energy systems and biomaterials optimization. Key Article Trends (2020–2024): His publications emphasize nanomaterial synthesis (e.g., selenium/ resveratrol composites), cancer drug design via metal complexes, and hydrogen storage materials. He frequently employs X-ray crystallography, DFT simulations, and in vitro biological assays to validate findings. Advising & Grants: No explicitly listed students or grants, though his collaborative research networks suggest involvement in multi-institutional projects (e.g., with centers in Serbia, EU, and China). Labs & Teams: Associated with biomaterials and materials science research groups at the Faculty of Engineering, contributing to initiatives like selenium nanoparticle stabilization and AI-driven material design.

• Applied Informatics in Engineering
• Biomedical Engineering
• Machine Learning
• +۳ مورد دیگر

Tijana Geroski is an Assistant Professor at the Faculty of Engineering, University of Kragujevac, Serbia, affiliated with the Department of Applied Mechanics and Automatic Control. Her work integrates applied informatics in engineering with biomedical applications, focusing on machine learning, medical imaging, and computational modeling. She contributes to interdisciplinary research through platforms like SGABU, designed to bridge education and research in multiscale modeling. Her research spans topics such as predictive modeling for cardiovascular diseases, AI-driven diagnostic systems (e.g., lung and spine imaging analysis), and simulation tools for personalized medicine. Notable projects include developing FPGA-based expert systems for disc hernia diagnosis and optimizing drug delivery via computational fluid dynamics. She also explores the intersection of big data, materials science, and epidemiological modeling, particularly in the context of the SARS-CoV-2 pandemic. Her articles emphasize interdisciplinary collaboration, coupling experimental data with numerical simulations. Recent work highlights advancements in transfer learning for medical imaging, physics-driven deep learning for thermal modeling, and FPGA implementations of Bayesian diagnostic systems. These contributions underscore her commitment to advancing healthcare through computational and AI-based solutions. She collaborates actively with institutes like SARTIK (Steinbeis Advanced Risk Technologies Institute) to enhance biomaterial risk assessment and in-silico clinical trials. Her teaching and research roles reflect a dedication to both academic innovation and practical biomedical engineering applications.

Milan Despotovićمشاهده پروفایل

استاد

• Renewable Energy Systems
• Solar Energy Forecasting
• Energy Efficiency in Buildings
• +۳ مورد دیگر

Milan Despotović serves as a Professor in the Department for Energy and Process Engineering at the Faculty of Engineering, University of Kragujevac. His academic career spans over a decade since his election in 2012, with research deeply rooted in energy systems and sustainable engineering practices. His primary research focuses on solar energy forecasting , renewable energy integration , and energy efficiency optimization across building systems and industrial processes. Notable work includes developing advanced metrics for solar irradiance prediction, analyzing electric vehicle adoption barriers in Serbia, and optimizing thermal performance in sports facilities repurposed as emergency warming shelters. His methodology frequently combines statistical modeling , artificial intelligence , and thermodynamic analysis to address energy challenges. Analysis of his recent publications reveals a strong trend toward hybrid forecasting systems that integrate machine learning with meteorological data, particularly for short-term solar irradiance prediction. His work increasingly addresses practical implementation challenges in Serbian energy infrastructure, including grid integration of renewables and sustainable transportation adoption. The interdisciplinary nature of his research bridges traditional process engineering with modern computational techniques. Professor Despotović maintains active research output through the SciDAR repository and contributes to the Faculty's mission in energy engineering education. His office (B-III-9) and direct contact (+381 34 335 990 ext. 730) indicate ongoing engagement with academic activities at the Kragujevac campus.

• Energy Engineering
• Process Engineering
• Renewable Energy Systems
• +۵ مورد دیگر

Vanja Šušteršič is a Professor at the Department of Energy and Process Engineering, Faculty of Engineering, University of Kragujevac. His research focuses on energy systems optimization, renewable energy integration, wastewater treatment technologies, and innovative educational tools like 3D printing in hydraulic engineering education. He holds a prominent role in advancing sustainable energy solutions for industrial and residential applications. Research interests include thermal energy recovery in food processing, biogas production through anaerobic co-digestion, geothermal heat pump systems, and energy efficiency in building heating/cooling. His work bridges theoretical analysis with practical implementation, emphasizing cost-benefit evaluations and environmental impact assessments. Recent scholarship highlights trends in waste-to-energy conversion, PM2.5 emission analysis post-fuel switching, and hydrostatic transmission technologies for mobile machinery. His articles often address interdisciplinary challenges at the intersection of mechanical engineering, environmental science, and sustainable urban development. No scientific awards explicitly listed. Active in teaching and curriculum development, though specific student advisees aren't documented in available texts. Laboratory involvement includes experimental setups for anaerobic digestion and thermal system performance testing.

• Thermodynamics
• Energy Engineering
• Renewable Energy Systems
• +۳ مورد دیگر

Prof. Danijela Nikolić is a Professor at the Department for Energy and Process Engineering, Faculty of Engineering, University of Kragujevac. Her research focuses on thermodynamics, energy systems optimization, and sustainable building design. She holds a professorship since 2020 and chairs the Energy and Process Engineering division. Her work emphasizes renewable energy integration, energy efficiency in buildings, and climate change mitigation strategies in Serbia. She collaborates with SciDAR for publications and is affiliated with the Faculty's Department for Energy and Process Engineering, office D-28. Research interests include solar energy applications, building energy systems, and lifecycle assessment. She has conducted case studies on ventilation strategies, photovoltaic systems, and waste management in automotive industries. Her work combines numerical analysis with multi-criteria decision-making frameworks to address energy challenges in Serbia’s urban and industrial sectors. Publications highlight innovations in solar greenhouse design, composite material optimization, and AI-driven energy management. She advocates for low-carbon urban development, emphasizing Serbia’s transition to sustainable energy systems. Her applied research bridges theoretical models with practical implementation in Kragujevac and beyond.

Миодраг Д. Спасићمشاهده پروفایل

دانشیار

• Control Systems
• Model Predictive Control
• Orthogonal Functions
• +۴ مورد دیگر

Миодраг Д. Спасић is an Associate Professor at the Department of Automatics, Faculty of Electronic Engineering, University of Niš, appointed in 2021. His academic journey includes completing his undergraduate studies at the same institution in 2010, followed by earning his PhD from the Norwegian University of Science and Technology in Trondheim in 2019. Dr. Spasić's research focuses on advanced control systems, particularly in the areas of model predictive control, orthogonal functions, neural networks, and nonlinear systems. His work demonstrates a strong emphasis on practical implementations for electromechanical systems, particularly DC motor control and magnetic levitation systems. He has developed innovative approaches combining orthogonal polynomial networks with traditional control methodologies to address challenges in robustness and real-time implementation. His publication record shows consistent productivity with 13 papers in journals with impact factors. Analysis of his recent publications reveals a clear research trajectory focused on enhancing control system performance through orthogonal function-based approaches, with increasing sophistication in handling nonlinearities and uncertainties in complex systems. His work bridges theoretical control concepts with practical engineering applications. Dr. Spasić is actively involved in research projects, currently participating in 2 national and 1 international projects. His collaborative work with colleagues at the University of Niš demonstrates strong institutional research connections within the Department of Automatics.

Dragan H. Stojanovicمشاهده پروفایل

استاد

• Artificial Intelligence
• Edge Computing
• Smart City Systems
• +۳ مورد دیگر

Dr. Dragan H. Stojanovic serves as a Professor at the Faculty of Electronic Engineering, University of Niš, specializing in computer science since his appointment in 2015. His academic journey spans over three decades at the same institution, where he earned all his degrees and now leads research in cutting-edge computational fields. His educational background includes: PhD in Electrical Engineering and Computing (2004, University of Niš) MSc in Electrical Engineering and Computing (1998, University of Niš) BSc in Electrical Engineering and Computing (1993, University of Niš) Stojanovic's research centers on Artificial Intelligence and Edge Computing , with significant contributions to Smart City infrastructures, parallel processing algorithms, and neural network applications. His work bridges theoretical computer science with practical implementations in urban systems, demonstrated through collaborations on 1 national and 2 international research projects . Recent publications (2022-2025) reveal a strategic pivot toward resource-constrained AI deployment across edge-fog architectures, while earlier work (2014-2020) established foundations in geospatial data parallelization and social media analytics. His publication portfolio shows evolving expertise: 2014-2019: Focus on parallel computing (MPI/CUDA) for geospatial and trajectory data 2020-2022: Integration of IoT and social networks into Smart City business processes 2023-2025: Advanced Edge AI optimization for real-time object detection and prediction systems Stojanovic actively contributes to academic knowledge transfer through project leadership and has published 16 journal articles in impact-factor journals. His current research trajectory emphasizes sustainable computing paradigms for urban environments, with emerging work on competency-focused AI education frameworks.

Olivera R. Pronić Rančićمشاهده پروفایل

استاد

• Telecommunications
• Microwave Engineering
• Artificial Neural Networks
• +۳ مورد دیگر

Olivera R. Pronić Rančić serves as a Full Professor at the Faculty of Electronic Engineering, University of Nis, specializing in Telecommunications with expertise in microwave engineering and neural network applications. Her academic leadership spans over a decade since her 2013 promotion to full professorship, with deep institutional roots at Serbia's premier electronics education center. Her educational foundation includes: PhD in Telecommunications (2002, Faculty of Electronic Engineering, University of Nis) Master's degree in Telecommunications (1998, same institution) Bachelor's degree in Electronics and Telecommunications (1993) Research focuses on the intersection of microwave engineering and artificial intelligence, particularly ANN-driven solutions for noise modeling in semiconductor devices, RF MEMS design, and antenna array optimization. Her methodology bridges theoretical electrical engineering with practical wireless communication systems, emphasizing neural network validation through experimental semiconductor characterization and wearable antenna development. Analysis of her publication trajectory reveals consistent innovation in applying neural networks to microwave transistor modeling since 2013, with recent work expanding into smart textile antennas and gallium nitride HEMT noise analysis. Her research demonstrates strong continuity in solving electromagnetic compatibility challenges through machine learning, maintaining relevance in both academic and industrial semiconductor applications. Key recognition includes: Aleksandar Marinčić award (2012) for exceptional contributions to microwave technique She actively directs research funding with current involvement in one national and one international project, building on extensive prior leadership in 10 national scientific research projects and 2 Ministry of Science higher-education development initiatives. Her international collaboration portfolio includes 2 DAAD research projects and 8 ERASMUS+/TEMPUS teaching reform initiatives across European institutions. As an integral member of the Faculty's Telecommunications Department research ecosystem, she contributes to conference leadership through ICEST and TELSIKS committees, recently organizing the 2023 TELSIKS special session on Applied Artificial Intelligence Learning Requirements while maintaining active laboratory work in microwave device characterization.

Mladen Veinovićمشاهده پروفایل

استاد

• Cybersecurity
• Network Security
• Data Protection
• +۷ مورد دیگر

Mladen Veinović is a Professor at Singidunum University, affiliated with the Faculty of Informatics and Computing. He holds a Doctorate in Systems Theory from the School of Electrical Engineering, University of Belgrade (1996), along with postgraduate and bachelor's degrees in Automatic Control and Electronics from the same institution. His research focuses on cybersecurity, network security, signal processing, and machine learning. Key contributions include work on malware C2 infrastructure, IoT security, steganography, and robust speech processing algorithms. He has authored/co-authored over 40 books and numerous peer-reviewed articles in journals like IEEE Access, Sensors, and Computers & Security. Veinović is actively involved in education technology, developing secure communication tools and problem-based learning approaches. His work spans cloud forensics, blockchain applications, and pandemic-era teaching strategies. He has been a key contributor to projects like the WIDE web development framework and cybersecurity tools for sensor networks. Professional affiliations include Singidunum University's research institute and participation in international conferences like Sinteza and TELSIKS. His expertise bridges theoretical research and practical applications in digital systems, IoT, and information security.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۸ مورد دیگر

Marina Marjanović is a Professor at Singidunum University's Faculty of Informatics and Computing, specializing in Artificial Intelligence, Machine Learning, and Computer Vision. Her research focuses on neural networks, adversarial attacks, emotion recognition, and healthcare applications. She holds a PhD in Signals and Systems from Universidad Politécnica de Madrid (2007) and has extensive publications in top-tier journals and conferences. Her work spans diverse areas: developing datasets for Serbian Sign Language, addressing demographic bias in age estimation models, and optimizing algorithms for phishing detection. Notable contributions include hybrid machine learning models for early disease diagnostics and adversarial attack vulnerability analysis on CNNs. Research Highlights : Age Estimation Bias Mitigation Chest X-ray Abnormalities Detection Phishing Website Detection Healthcare 4.0 Applications Marjanović collaborates on EU-funded projects and actively participates in international conferences like IEEE FG and Sinteza. She chairs Singidunum’s AI research group, focusing on explainable AI and ethical machine learning practices.

• Wireless Communications
• Signal Processing
• Fading Channel Analysis
• +۴ مورد دیگر

Petar Spalević is a professor at Singidunum University within the School of Electrical Engineering and Computing. With over two decades of academic contributions from 2002 to 2025, he has established himself as a significant researcher in electrical engineering and computer science disciplines. His research expertise spans multiple interconnected domains: Wireless communications and fading channel modeling Free space optical (FSO) transmission systems Machine learning applications for healthcare diagnostics Educational technology and innovative teaching methodologies Dr. Spalević's publication trajectory reveals an evolution from foundational work in signal processing and wireless communications to more contemporary applications integrating machine learning techniques. His recent publications (2023-2025) demonstrate a strong focus on healthcare applications including Parkinson's detection from gait analysis, respiratory condition classification from audio, and ECG anomaly detection using advanced neural network architectures. He maintains parallel research in wireless communications, particularly examining FSO system performance under various atmospheric turbulence conditions. His work bridges theoretical communication engineering with practical applications across healthcare, transportation, and education sectors. This multidisciplinary approach while maintaining technical depth in core engineering principles characterizes his research philosophy.

• Cryptography
• Information Security
• Educational Technology
• +۳ مورد دیگر

Violeta Tomašević is a Professor at Singidunum University, Faculty of Informatics and Computing. Her research focuses on cryptography, cybersecurity, educational technology, and data analysis. She holds a Doctorate in Cryptanalysis from the School of Electrical Engineering, University of Belgrade (2005), and has extensive experience in software development for educational and security applications. Her work includes designing anti-plagiarism systems, optimizing RFID protocols, and developing tools for student progress analysis using machine learning. She has authored multiple textbooks on computer architecture and programming, and her contributions span over 50 peer-reviewed articles and conference papers. Education: Doctoral studies: Cryptanalysis, School of Electrical Engineering, University of Belgrade (1998-2005) Postgraduate: Digital Signal Processing, School of Electrical Engineering, University of Belgrade (1988-1994) Bachelor's: Electronics, School of Electrical Engineering, University of Belgrade (1983-1988) Research Interests: Cryptography (RC4, RFID security), educational software development, cybersecurity in insurance, and decision support systems. Her work integrates theoretical cryptanalysis with practical applications in industry and academia. Grants & Projects: Led projects on cloud computing in business, investment decision support systems (e.g., FIDES), and virtual labs for computing courses. Collaborated with institutions like the University of Belgrade and industry partners on software tools for risk management and academic integrity. Labs/Teams: Involved in Singidunum's research groups focused on cybersecurity, educational technology, and algorithm optimization. Active in organizing conferences like Sinteza and SITCON.

• Machine Learning
• Cybersecurity
• Artificial Intelligence
• +۴ مورد دیگر

Miodrag Živković is a Professor at Singidunum University, specializing in Computer Science and Artificial Intelligence. He holds a Doctorate in Software Engineering from the School of Electrical Engineering, Belgrade (2008–2014) and a Bachelor's in Computer Science and Informatics (2001–2007). His research focuses on machine learning, cybersecurity, and metaheuristic optimization, with applications in IoT, healthcare, and environmental monitoring. He has authored multiple books on software development and programming, including Razvoj mobilnih aplikacija (2020) and Programski jezici, veb servisi i napredno Java programiranje (2019). Education: Doctorate in Software Engineering (School of Electrical Engineering, Belgrade), Bachelor's in Computer Science and Informatics. Research Interests: Cybersecurity frameworks, neural networks, explainable AI, IoT systems, and optimization algorithms for predictive modeling. His work spans over 150 peer-reviewed articles, including studies on metaverse security, waste classification using CNNs, and benzene pollution forecasting. He frequently publishes in journals like Engineering Applications of Artificial Intelligence and Applied Soft Computing , and contributes to conferences such as Sinteza and IEEE Symposia. Key projects include developing AI-driven intrusion detection systems, optimizing renewable energy forecasts via hybrid networks, and leveraging metaheuristics for medical diagnostics. His interdisciplinary approach bridges theoretical advancements with practical applications in smart grids, healthcare, and environmental sustainability.